Evidence map›Paper›PMID 38686881›Full record

ArticleJournal of the American Heart Association2024

Evaluation of a Clinical Decision Support Tool to Guide Adoption of the American Heart Association Telemetry Monitoring Practice Standards.

Allen Bergstedt, Brian Hilliard, Sarah Alabsi, Michael G Usher, Maya Peters, James Grace, Genevieve B Melton, Timothy J Beebe, Deborah L Pestka

Abstract readEvaluation Study
In one paragraph

Article in Journal of the American Heart Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Allen BergstedtDepartment of Medicine University of Minnesota Medical School Minneapolis MN.ORCID 0009-0003-4551-3096
Brian HilliardDepartment of Medicine University of Minnesota Medical School Minneapolis MN.ORCID 0000-0002-4901-6594
Sarah AlabsiDepartment of Medicine University of Minnesota Medical School Minneapolis MN.
Michael G UsherDepartment of Medicine University of Minnesota Medical School Minneapolis MN.
Maya PetersCenter for Learning Health System Sciences University of Minnesota Medical School Minneapolis MN.ORCID 0000-0003-4603-1948
James GraceDepartment of Medicine University of Minnesota Medical School Minneapolis MN.
Genevieve B MeltonDepartment of Surgery University of Minnesota Medical School Minneapolis MN.ORCID 0000-0001-5193-1663
Timothy J BeebeCenter for Learning Health System Sciences University of Minnesota Medical School Minneapolis MN.ORCID 0000-0003-4700-2436
Deborah L PestkaCenter for Learning Health System Sciences University of Minnesota Medical School Minneapolis MN.ORCID 0000-0001-5096-1991

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe objectives of this study were to (1) evaluate telemetry use pre- and postimplementation of clinical decision support tools to support American Heart Association practice standards for telemetry monitoring and (2) understand the factors that may contribute to variation of telemetry monitoring in practice. METHODS AND

resultsFirst, we captured overall variability in telemetry use pre- and postimplementation of the clinical decision support intervention. We then conducted semistructured interviews with telemetry-ordering providers to identify key barriers and facilitators to adoption. During the study period, 399 physicians met criteria for inclusion and were divided into excessive and nonexcessive orderers. Distribution of telemetry use was bimodal. Among nonexcessive users, 24.4% of patient days were with telemetry compared with 51.6% among excessive users. On average, both excessive (6.1% reduction) and nonexcessive users (2.8% reduction) decreased telemetry use postimplementation, and these reductions were sustained over a 16-month period. Sixteen interviews were conducted. Physicians believed that the tool was successful because it caused them to more closely consider if telemetry was indicated for each patient. Physicians also voiced frustration with interruptions to their workflow, and some noted that they commonly use telemetry outside of practice standards to monitor patients who were acutely but not critically ill.

conclusionsEmbedding telemetry practice standards into the electronic health record in the form of clinical decision support is effective at reducing excess telemetry use. Although the intervention was well received, there are persistent barriers, such as preexisting views on telemetry and existing workflow habits, that may inhibit higher adoption of standards.

Indexed as

American Heart AssociationDecision Support Systems, ClinicalPractice Patterns, Physicians'TelemetryAttitude of Health PersonnelGuideline AdherenceHumansMalePractice Guidelines as TopicUnited Statesclinical decision supportimplementation sciencetelemetry

Identifiers

PMID38686881
PMCPMC11179861

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.